AI screening tool for a recruitment software
By CHILDISH.AI
Client
Recruit
Services
Project Description
Challenge: The client was a start-up solving the problem of managing numerous processes within a recruitment department. Hiring teams have data from a variety of sources, which are difficult to process and analyze. Our task was to structure and develop a data structure and create an AI tool for screening candidates. Approach: Our work started with a research and development phase. A core task was to develop a solution architecture which meets the requirements of the AI models but of the applications which collected the data already. We identified an AWS structure combined with other ETL tools to be able to work with different formats from multiple sources. Once the data structure and pipelines were set, we used semantic analysis with NLTK preprocessing to extract the necessary information for each candidate. The ML part was further developed with Keras, TensorFlow and SKLearn. The output presented a structured database of candidates and sorted them into predefined groups and factors.Key Metrics: As a result of the project, applicants in the system are automatically shortlisted and hiring teams need only allocate strategic efforts to review the screening results which proved to have up to 83% more accuracy. The solutions achieved their goal to optimize the recruitment process and reduce the time invested in a manual review of the CVs of candidates by half and thus increase the productivity of a recruitment team using the application.
Challenge: The client was a start-up solving the problem of managing numerous processes within a recruitment department. Hiring teams have data from a variety of sources, which are difficult to process and analyze. Our task was to structure and develop a data structure and create an AI tool for screening candidates. Approach: Our work started with a research and development phase. A core task was to develop a solution architecture which meets the requirements of the AI models but of the applications which collected the data already. We identified an AWS structure combined with other ETL tools to be able to work with different formats from multiple sources. Once the data structure and pipelines were set, we used semantic analysis with NLTK preprocessing to extract the necessary information for each candidate. The ML part was further developed with Keras, TensorFlow and SKLearn. The output presented a structured database of candidates and sorted them into predefined groups and factors.Key Metrics: As a result of the project, applicants in the system are automatically shortlisted and hiring teams need only allocate strategic efforts to review the screening results which proved to have up to 83% more accuracy. The solutions achieved their goal to optimize the recruitment process and reduce the time invested in a manual review of the CVs of candidates by half and thus increase the productivity of a recruitment team using the application.
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